{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacked Bar Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of creating a stacked bar plot with error bars using `~matplotlib.pyplot.bar`.  Note the parameters *yerr* used for error bars, and *bottom* to stack the women's bars on top of the men's bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# matplot(矩阵图)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 5\n",
    "menMeans = (20,35,30,35,27)\n",
    "womenMeans = (25,32,34,20,25)\n",
    "menStd = (2,3,4,1,2)\n",
    "womenStd = (3,5,2,3,3)\n",
    "\n",
    "ind = np.arange(N) # the x locations for the group\n",
    "width = 0.35 # the width of the bars: can also be len(x) sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = plt.bar(ind, menMeans, width, yerr=menStd)\n",
    "p2 = plt.bar(ind, womenMeans, width, bottom=menMeans, yerr=womenStd)\n",
    "\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Scores by group and gender')\n",
    "plt.xticks(ind, ('group1','group2','group3','group4','group5'))\n",
    "plt.yticks(np.arange(0,81,10))\n",
    "plt.legend((p1[0],p2[0]),('men','women'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
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